使用TensorFlow和tf.data.Dataset对文件夹中的图像序列进行采样

import tensorflow as tf
import os

image_folder = 'path/to/image/folder'
image_format = '*.jpg'  # 可根据实际情况修改文件格式

image_files = tf.data.Dataset.list_files(os.path.join(image_folder, image_format))

def preprocess_image(file_path):
    # 读取图像文件
    image = tf.io.read_file(file_path)
    # 解码图像文件
    image = tf.image.decode_jpeg(image, channels=3)
    # 对图像进行预处理，例如缩放、裁剪、归一化等
    image = preprocess(image)
    return image

dataset = image_files.map(preprocess_image)

window_size = 10
stride = 5

dataset = dataset.window(window_size, stride=stride, drop_remainder=True)

dataset = dataset.flat_map(lambda x: x.batch(window_size))

dataset = dataset.shuffle(buffer_size=1000)

batch_size = 32
dataset = dataset.batch(batch_size)

dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)